Prediction of Compressibility of Overconsolidated Uncemented Soils

Overconsolidated soils exhibit a bilinear e-log p relationship. During virgin compression, microstructural units form larger stable groups, thereby reducing the operating specific surface and, in turn, net osmotic repulsive forces in the soil. The rebound portion of the e-log p curve is consequently flatter. The generalized relationship for compressibility of uncemented soils in the overconsolidated state has been developed in the form of e/eL = 1.122 = 0.188 log pc - 0.0463 log p in which e/eL is the generalized soil state parameter, pc is the preconsolidation pressure in kPa, p is the effective overburden pressure in kPa, e is the in situ void ratio, and eL is the void ratio corresponding to the liquid limit water content (wLG). This relationship can be usefully employed to predict both the preconsolidation pressure and compressibility responses of overconsolidated uncemented soils.

Prediction of Breakdown Voltages of Binary Gas Mixtures in Uniform Electric Fields

A simple equation to predict the breakdown voltages for binary mixtures (Vmix) of electronegative gases (SF6, CCl2F2) and buffer gases (N2, N2O, CO2, air) under uniform electric field has been proposed. Values of Vmix evaluated using this equation for mixtures of SF6-N2, SF6-air, SF6-N2O, SF6-CO2 and CCl2F2-N2 over a wide range of pd show an excellent agreement with the experimentally measured data available in the literature.

Simultaneous prediction of acidity parameters (pH and titratable acidity) in Kefir using near infrared reflectance spectroscopy.

Acidity in terms of pH and titratable acids influences the texture and flavour of fermented dairy products, such as Kefir. However, the methods for determining pH and titratable acidity (TA) are time consuming. Near infrared (NIR) spectroscopy is a non-destructive method, which simultaneously predicts multiple traits from a single scan and can be used to predict pH and TA. The best pH NIR calibration model was obtained with no spectral pre-treatment applied, whereas smoothing was found to be the best pre-treatment to develop the TA calibration model. Using cross-validation, the prediction results were found acceptable for both pH and TA. With external validation, similar results were found for pH and TA, and both models were found to be acceptable for screening purposes.

Markers of systemic inflammation in diagnostics and in prediction of outcome of community-acquired infection

Sepsis is associated with a systemic inflammatory response. It is characterised by an early proinflammatory response and followed by a state of immunosuppression. In order to improve the outcome of patients with infection and sepsis, novel therapies that influence the systemic inflammatory response are being developed and utilised. Thus, an accurate and early diagnosis of infection and evaluation of immune state are crucial. In this thesis, various markers of systemic inflammation were studied with respect to enhancing the diagnostics of infection and of predicting outcome in patients with suspected community-acquired infection. A total of 1092 acutely ill patients admitted to a university hospital medical emergency department were evaluated, and 531 patients with a suspicion of community-acquired infection were included for the analysis. Markers of systemic inflammation were determined from a blood sample obtained simultaneously with a blood culture sample on admission to hospital. Levels of phagocyte CD11b/CD18 and CD14 expression were measured by whole blood flow cytometry. Concentrations of soluble CD14, interleukin (IL)-8, and soluble IL-2 receptor α (sIL-2Rα) were determined by ELISA, those of sIL-2R, IL-6, and IL-8 by a chemiluminescent immunoassay, that of procalcitonin by immunoluminometric assay, and that of C-reactive protein by immunoturbidimetric assay. Clinical data were collected retrospectively from the medical records. No marker of systemic inflammation, neither CRP, PCT, IL-6, IL-8, nor sIL-2R predicted bacteraemia better than did the clinical signs of infection, i.e., the presence of infectious focus or fever or both. IL-6 and PCT had the highest positive likelihood ratios to identify patients with hidden community-acquired infection. However, the use of a single marker failed to detect all patients with infection. A combination of markers including a fast-responding reactant (CD11b expression), a later-peaking reactant (CRP), and a reactant originating from inflamed tissues (IL-8) detected all patients with infection. The majority of patients (86.5%) with possible but not verified infection showed levels exceeding at least one cut-off limit of combination, supporting the view that infection was the cause of their acute illness. The 28-day mortality of patients with community-acquired infection was low (3.4%). On admission to hospital, the low expression of cell-associated lipopolysaccharide receptor CD14 (mCD14) was predictive for 28-day mortality. In the patients with severe forms of community-acquired infection, namely pneumonia and sepsis, high levels of soluble CD14 alone did not predict mortality, but a high sCD14 level measured simultaneously with a low mCD14 raised the possibility of poor prognosis. In conclusion, to further enhance the diagnostics of hidden community-acquired infection, a combination of inflammatory markers is useful; 28-day mortality is associated with low levels of mCD14 expression at an early phase of the disease.

Prediction of bubble growth rates and departure volumes in nucleate boiling at isolated sites

A model has been developed to predict heat transfer rates and sizes of bubbles generated during nucleate pool boiling. This model assumes conduction and a natural convective heat transfer mechanism through the liquid layer under the bubble and transient conduction from the bulk liquid. The temperature of the bulk liquid in the vicinity of the bubble is obtained by assuming a turbulent natural convection process from the hot plate to the liquid bulk. The shape of the bubble is obtained by equilibrium analysis. The bubble departure condition is predicted by a force balance equation. Good agreement has been found between the bubble radii predicted by the present theory and the ones obtained experimentally.

Fuel conditions and fire behavior characteristics of managed Picea abies and Pinus sylvestris forests in Finland

The objectives of this study were to analyze the impact of structural stand characteristics on ignition potential, surface fuel moisture, and fire behavior in Pinus sylvestris L. and Picea abies (L.) Karst stands in Finland and to explain stand-specific fire danger using the Canadian Fire Weather Index System and the Finnish Fire Risk Index. Additionally, the study analyzes the relationship between observed fire activity and fire weather indices at different stages of growing season. Field experiments were carried out in Pinus sylvestris or Picea abies dominated stands during fire seasons 2001 and 2002. Observations on ignition potential, fuel moisture, and fire behavior were analyzed in relation to stand structure and the outputs of the Finnish and Canadian fire weather indices. Seasonal patterns of fire activity were examined based on national fire statistics 1996 2003, effective temperature sum, and the fire weather indices. Point fire ignition potential was highest in Pinus clear-cuts and lowest in closed Picea stands. Moss-dominated surface fuels were driest in clear-cut and sapling stage stands and presented the highest moisture content under closed Picea canopy. Pinus sylvestris stands carried fire under a wide range of fire weather conditions under which Picea abies stands failed to sustain fire. In the national fire records, the daily number of reported ignitions presented its highest value during late fire season whereas the daily area burned peaked most substantially during early season. The fire weather indices correlated significantly with ignition potential and fuel moisture but were unable to explain fire behavior in the experimental fires. During the initial and final stages of the growing season, fire activity was disconnected from weather-based fire danger ratings. Information on stand structure and season stage would benefit the assessment of fire danger in Finnish forest landscape for fire suppression and controlled burning purposes.

Prediction of frequency and amplitude of foundations at resonance

This note presents the statistical analysis carried out on some of the available experimental results to predict the resonant frequency and maximum displacement amplitude of a machine foundation – soil system under vertical vibration as a function of the size and weight of the foundation and of the excitation level. A total of 442 experimental results of Fry, Novak, and Raman have been analysed using nonlinear regression analysis. The results obtained compared well with predictions obtained from the popular theoretical models, and the coefficient of correlation obtained from the analysis was satisfactory in most of the cases.

Effects of seasonal variability on FT-NIR prediction of dry matter content for whole Hass avocado fruit

Fourier Transform (FT)-near infra-red spectroscopy (NIRS) was investigated as a non-invasive technique for estimating percentage (%) dry matter of whole intact 'Hass' avocado fruit. Partial least squares (PLS) calibration models were developed from the diffuse reflectance spectra to predict % dry matter, taking into account effects of seasonal variation. It is found that seasonal variability has a significant effect on model predictive performance for dry matter in avocados. The robustness of the calibration model, which in general limits the application for the technique, was found to increase across years (seasons) when more seasonal variability was included in the calibration set. The R-v(2) and RMSEP for the single season prediction models predicting on an independent season ranged from 0.09 to 0.61 and 2.63 to 5.00, respectively, while for the two season models predicting on the third independent season, they ranged from 0.34 to 0.79 and 2.18 to 2.50, respectively. The bias for single season models predicting an independent season was as high as 4.429 but <= 1.417 for the two season combined models. The calibration model encompassing fruit from three consecutive years yielded predictive statistics of R-v(2) = 0.89, RMSEP = 1.43% dry matter with a bias of -0.021 in the range 16.1-39.7% dry matter for the validation population encompassing independent fruit from the three consecutive years. Relevant spectral information for all calibration models was obtained primarily from oil, carbohydrate and water absorbance bands clustered in the 890-980, 1005-1050, 1330-1380 and 1700-1790 nm regions. These results indicate the potential of FT-NIRS, in diffuse reflectance mode, to non-invasively predict the % dry matter of whole 'Hass' avocado fruit and the importance of the development of a calibration model that incorporates seasonal variation. Crown Copyright (c) 2012 Published by Elsevier B.V. All rights reserved.

Prediction of breeding values for average fruit weight in mango using a multivariate individual mixed model

Mango is an important horticultural fruit crop and breeding is a key strategy to improve ongoing sustainability. Knowledge of breeding values of potential parents is important for maximising progress from breeding. This study successfully employed a mixed linear model methods incorporating a pedigree to predict breeding values for average fruit weight from highly unbalanced data for genotypes planted over three field trials and assessed over several harvest seasons. Average fruit weight was found to be under strong additive genetic control. There was high correlation between hybrids propagated as seedlings and hybrids propagated as scions grafted onto rootstocks. Estimates of additive genetic correlation among trials ranged from 0.69 to 0.88 with correlations among harvest seasons within trials greater than 0.96. These results suggest that progress from selection for broad adaptation can be achieved, particularly as no repeatable environmental factor that could be used to predict G x E could be identified. Predicted breeding values for 35 known cultivars are presented for use in ongoing breeding programs.

Prediction of mortality and conception rates of beef breeding cattle in northern Australia

In current simulation packages for the management of extensive beef-cattle enterprises, the relationships for the key biological rates (namely conception and mortality) are quite rudimentary. To better estimate these relationships, cohort-level data covering 17 100 cow-years from six sites across northern Australia were collated and analysed. Further validation data, from 7200 cow-years, were then used to test these relationships. Analytical problems included incomplete and non-standardised data, considerable levels of correlation among the 'independent' variables, and the close similarity of alternate possible models. In addition to formal statistical analyses of these data, the theoretical equations for predicting mortality and conception rates in the current simulation models were reviewed, and then reparameterised and recalibrated where appropriate. The final models explained up to 80% of the variation in the data. These are now proposed as more accurate and useful models to be used in the prediction of biological rates in simulation studies for northern Australia. © The State of Queensland (through the Department of Agriculture, Fisheries and Forestry) 2012. © CSIRO.

Improving the prediction of odour impacts from meat chicken farms with detailed ventilation and odour data

Odour from meat chicken (broiler) farms is an environmental issue affecting the sustainable development of the chicken meat industry but is a normal part of broiler production. Odour plumes exhausted from broiler sheds interact with the environment, where dispersion and dilution of the odours varies constantly, especially diurnally. The potential for odour impacts is greatest when odour emission rates are high and/or when atmospheric dispersion and dilution of odour plumes is limited (i.e. during stable conditions). We continuously monitored ventilation rate, on-site weather conditions, atmospheric stability, and estimated odour concentration with an artificial olfaction system. Detailed inspection of odour emission rates at critical times, i.e. dawn, dusk and night time, revealed that maximum daily and batch odour emission rates are not necessarily the cause of odour impacts. Periods of lower odour emission rates on each day are more likely to correspond with odour impacts. Odour emission rates need to be measured at the times when odour impacts are most likely to occur, which is likely to be at night. Additionally, high resolution ventilation rate data should be sought after to improve odour emission models, especially at critical times of the day. Consultants, regulators and researchers need to give more thought to odour emission rates from meat chicken farms to improved prediction and management of odour impacts.

High-throughput prediction of eucalypt lignin syringyl/guaiacyl content using multivariate analysis: a comparison between mid-infrared, near-infrared, and Raman spectroscopies for model development

BACKGROUND: In order to rapidly and efficiently screen potential biofuel feedstock candidates for quintessential traits, robust high-throughput analytical techniques must be developed and honed. The traditional methods of measuring lignin syringyl/guaiacyl (S/G) ratio can be laborious, involve hazardous reagents, and/or be destructive. Vibrational spectroscopy can furnish high-throughput instrumentation without the limitations of the traditional techniques. Spectral data from mid-infrared, near-infrared, and Raman spectroscopies was combined with S/G ratios, obtained using pyrolysis molecular beam mass spectrometry, from 245 different eucalypt and Acacia trees across 17 species. Iterations of spectral processing allowed the assembly of robust predictive models using partial least squares (PLS). RESULTS: The PLS models were rigorously evaluated using three different randomly generated calibration and validation sets for each spectral processing approach. Root mean standard errors of prediction for validation sets were lowest for models comprised of Raman (0.13 to 0.16) and mid-infrared (0.13 to 0.15) spectral data, while near-infrared spectroscopy led to more erroneous predictions (0.18 to 0.21). Correlation coefficients (r) for the validation sets followed a similar pattern: Raman (0.89 to 0.91), mid-infrared (0.87 to 0.91), and near-infrared (0.79 to 0.82). These statistics signify that Raman and mid-infrared spectroscopy led to the most accurate predictions of S/G ratio in a diverse consortium of feedstocks. CONCLUSION: Eucalypts present an attractive option for biofuel and biochemical production. Given the assortment of over 900 different species of Eucalyptus and Corymbia, in addition to various species of Acacia, it is necessary to isolate those possessing ideal biofuel traits. This research has demonstrated the validity of vibrational spectroscopy to efficiently partition different potential biofuel feedstocks according to lignin S/G ratio, significantly reducing experiment and analysis time and expense while providing non-destructive, accurate, global, predictive models encompassing a diverse array of feedstocks.

Prediction of masonry compressive behaviour using a damage mechanics inspired modelling method

Masonry under compression is affected by the properties of its constituents and their interfaces. In spite of extensive investigations of the behaviour of masonry under compression, the information in the literature cannot be regarded as comprehensive due to ongoing inventions of new generation products – for example, polymer modified thin layer mortared masonry and drystack masonry. As comprehensive experimental studies are very expensive, an analytical model inspired by damage mechanics is developed and applied to the prediction of the compressive behaviour of masonry in this paper. The model incorporates a parabolic progressively softening stress-strain curve for the units and a progressively stiffening stress-strain curve until a threshold strain for the combined mortar and the unit-mortar interfaces is reached. The model simulates the mutual constraints imposed by each of these constituents through their respective tensile and compressive behaviour and volumetric changes. The advantage of the model is that it requires only the properties of the constituents and considers masonry as a continuum and computes the average properties of the composite masonry prisms/wallettes; it does not require discretisation of prism or wallette similar to the finite element methods. The capability of the model in capturing the phenomenological behaviour of masonry with appropriate elastic response, stiffness degradation and post peak softening is presented through numerical examples. The fitting of the experimental data to the model parameters is demonstrated through calibration of some selected test data on units and mortar from the literature; the calibrated model is shown to predict the responses of the experimentally determined masonry built using the corresponding units and mortar quite well. Through a series of sensitivity studies, the model is also shown to predict the masonry strength appropriately for changes to the properties of the units and mortar, the mortar joint thickness and the ratio of the height of unit to mortar joint thickness. The unit strength is shown to affect the masonry strength significantly. Although the mortar strength has only a marginal effect, reduction in mortar joint thickness is shown to have a profound effect on the masonry strength. The results obtained from the model are compared with the various provisions in the Australian Masonry Structures Standard AS3700 (2011) and Eurocode 6.

High-Throughput Prediction of Acacia and Eucalypt Lignin Syringyl/Guaiacyl Content Using FT-Raman Spectroscopy and Partial Least Squares Modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias, in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. This research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

Prediction of Intrinsic kinetics in diffusion disguised immobilized enzyme systems

When immobilized enzyme kinetics are disguised by inter- and intraparticle diffusion effects, an approximate mathematical procedure is indicated whereby experimental data obtained in the limiting ranges of first- and zeroth-order Michaelis-Menten kinetics could be used for the prediction of the kinetic constants.